Transmission Error Probability Research Articles

Radar-Enabled Ambient Backscatter Communications

In this work, we exploit the radar clutter (i.e., the ensemble of echoes generated by the terrain and/or the surrounding objects in response to the signal emitted by a radar transmitter) as a carrier signal to enable an ambient backscatter communication from a source (tag) to a destination (reader). The proposed idea relies on the fact that, since the radar excitation is periodic, the radar clutter is itself periodic over time scales shorter than the coherence time of the environment. Upon deriving a convenient signal model, we propose two encoding/decoding schemes that do not require any coordination with the radar transmitter or knowledge of the radar waveform. Different tradeoffs in terms of transmission rate and error probability can be obtained upon changing the control signal driving the tag switch or the adopted encoding rule; also, multiple tags can be accommodated with either a sourced or an unsourced multiple access strategy. Some illustrative examples are provided.

Distribution of Age of Information in Status Update Systems With Heterogeneous Information Sources: An Absorbing Markov Chain-Based Approach

In this letter, we obtain the exact distributions of the Age of Information (AoI) and Peak AoI (PAoI) in a non-preemptive multi-source status update system for (i) Generate-At-Will (GAW) servers with probabilistic transmissions (ii) Random Arrival with Single Buffer (RA-SB) servers employing probabilistic buffer management, using absorbing Continuous-Time Markov Chains (CTMC). For both servers, the information sources are allowed to have different relative urgencies, phase-type service time distributions, and transmission error probabilities, for the sake of generality. Numerical examples are presented to validate the proposed analytical model.

Error Probability Analysis of Intelligent Reflecting Surface-Enabled Non-Orthogonal Multiple Access Systems

We analyze the error performance of intelligent reflecting surface (IRS)-aided non-orthogonal multiple access (NOMA) systems. We deduce novel approximate analytical expressions for average symbol error probability (SEP) for PAM and QAM. We also analyze the multi-user scenario by deriving the novel approximate analytical expressions for (i) the average SEP for multicast transmission and (ii) the average pairwise error probability (PEP) for unicast transmission. We consider Rayleigh fading direct path and independently Rician fading reflected paths from access point to users via IRS. We also formulate and solve a constrained optimization problem to determine the fraction of elements to be activated at any IRS in order to guarantee near-identical SEP performance for both the users. We present results to elucidate that adding few extra IRS elements yields SNR gain for both users, requires allocation of lower power to weaker user and supports communication with higher-order constellation without significant increase in SEP/PEP. Furthermore, a 3-bit programmable IRS yields near-identical performance as an IRS with infinite precision phase-shift. Impact of estimation error on average SEP is also investigated.

Performance of Multidevice Downlink Cell-Free System Under Finite Blocklength for uRLLC With Hard Deadlines

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and 500 μs latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

An Enhanced Energy-Efficient Data Collection Optimization Algorithm for UAV Swarm in the Intelligent Internet of Things

In the case of limited endurance of unmanned aerial vehicles (UAVs), in order to further improve UAV data collection efficiency, this paper puts forward EDC-UAVIIoT: an enhanced energy-efficient data collection optimization algorithm for UAV swarm in the intelligent Internet of Things. First of all, the algorithm optimizes the UAV cruise path through the intelligent Internet of Things routing mechanism, avoids the occurrence of data errors in the packet transmission process, and uses the end-to-end transmission error probability model. The error probability of data packets in the transmission process is calculated to improve the efficiency of data collection tasks and data throughput. Secondly, considering the relationship between energy harvesting and energy consumption balance, this paper uses semi-definite programming and a convex approximation algorithm to transform the non-convex optimization problem into a convex optimization problem and realize the mapping relationship between the UAV cluster node and the target node coordinates, which reduces the computational complexity. Finally, the simulation results show that the EDC-UAVIIoT algorithm is compared with other algorithms in network energy consumption, running time, network delay, and network throughput. The numerical values are increased by 7.03%, 10.16%, 12.39%, and 8.82%, respectively, thus verifying the effectiveness and stability of the proposed EDC-UAVIIoT algorithm.

Study of Uplink Resource Allocation for 5G IoT Services by Using Reinforcement Learning

<p>In order to support real time IoT services, the ultra Reliable and Low Latency Communications (uRLLC) was proposed in 5G wireless communication network. Different from the grant based access in 4G, the grant free technique is proposed in 5G to reduce the random access delay of uRLLC-required applications. This paper proposes the dedicated resource for exclusive access of individual UE and the shared resource pool for the contention of multiple UEs by adopting the reinforcement learning approach. The objective of this paper is to accomplish the uplink successful rate above 99.9% under certain transmission error probability. The proposed Prediction based Hybrid Resource Allocation (PHRA) scheme allocates the access resource in a heuristic manner by referring to the activity of UEs. The dedicated resource is mainly allocated to the high activity UEs and the initial transmission of UEs with medium activity while the shared resource pool is allocated for the re-transmission of medium activity UEs and low activity UEs by using the reinforcement learning model. The burst traffic model was applied during the exhaustive experiments. And the simulation results show that the proposed scheme achieves higher uplink packet delivery ratio and more effective resource utilization than the other schemes.</p> <p> </p>

Status Prediction and Data Aggregation for AoI-Oriented Short-Packet Transmission in Industrial IoT

Age of information (AoI) is an effective performance metric for time-critical industrial Internet of things (IIoT) applications. We investigate status prediction and data aggregation with prediction error awareness, to enhance the AoI performance for short-packet transmission (SPT) in time-critical IIoT. A predict-compare (PredComp) transmission scheme is proposed, where proactive transmission termination is employed in case of prediction error, by comparing the predicted and real updates at source. It is proved to achieve a significant average AoI performance gain over the case without prediction, even under high prediction error probability. In addition, a predict-aggregate-compare (PredAggComp) transmission scheme is proposed, where two status updates are predicted with different prediction horizons and aggregated by utilizing their time correlation. That allows a good tradeoff between the prediction accuracy and the transmission error probability. A closed-form threshold that the PredAggComp scheme outperforms the PredComp scheme is derived. Moreover, prediction horizon adaptation is conducted to minimize the average AoI of the proposed transmission schemes. Simulation results verify the analytical results and show the superiority of the proposed PredComp and PredAggComp schemes, with an average AoI reduction of up to 64% over the case without prediction.

Effective Energy Efficiency and Statistical QoS Provisioning Under Markovian Arrivals and Finite Blocklength Regime

In this article, we evaluate the effective energy efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited Internet of Things (IoT) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of effective capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters, such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms, such as Dinkelbach’s and cross entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that modified Dinkelbach’s algorithm has high performance and low complexity compared to others.

Age of information and energy efficiency of amplify‐and‐forward relay‐assisted Internet of Things with nonlinear energy harvesting and imperfect channel state information over Nakagami‐ fading channels

AbstractThe emergence of the fifth generation of communication networks (5G) has paved the way for the fast development of Internet of Things (IoT) as well as many real‐time and mission‐critical applications. Because the status update required to be as timely as possible, a new metric termed as age of information (AoI) was born with the ability of capturing the timeliness. However, in practical implementation the sensor is energy constrained and transmits very low‐power signals, which yield high AoI so that the received status update can be stale or even outdated. To overcome this problem, this work proposes an IoT system model by integrating energy harvesting (EH), amplify‐and‐forward (AF) relay cooperation, and short packet communication (SPC) techniques. In the proposed scheme, the relay that is connected to fixed power grid, transfers energy radio frequency signals to charge the sensor and assists forwarding status update by using time switching scheme. The employment of SPC overcomes the freshness loss caused by relay cooperation and EH. Then, this work investigates the AoI and energy efficiency of the proposed scheme under finite block length communications over Nakagami‐ fading channels by specially considering channel state information (CSI) and nonlinear EH model. Under these practical constraints, the tractable AF relay transmission model and the end‐to‐end approximated signal‐to‐noise ratio are found. The end‐to‐end block error probability of finite block length transmission is derived, too. Third, under the simultaneous consideration of nonlinear EH and outdated CSI, the statistical descriptions of the time duration for the sensor fully charging its battery are derived as well as the ones of the time intervals of update packet delivery. As a result, the average AoI is achieved with closed‐form expression and the tradeoff model of AoI to energy efficiency is established by simultaneously exploiting outdated CSIs and nonlinearity of EH circuit. The presented numerical analysis exploits the impact of the outdated CSIs and nonlinear EH model and gives the insights of such model.

Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks

In the era of Industry 4.0, there are many emerging industrial Internet of Things (IIoT) applications that require ultrareliable and low-latency communications (URLLCs), such as time-sensitive networking (TSN), status reporting, and remote control. With the deployment of massive intelligent devices in IIoT networks, providing security for device physical-layer access is a challenging issue, especially for URLLC applications with strict latency and reliability constraints. We thus developed an intelligent radio frequency identification (RFI) framework to provide a lightweight and energy-efficient physical-layer access scheme for URLLCs via leveraging unique hardware-level imperfections of transmitter. We propose a novel semisupervised-learning (SSL) algorithm to realize intelligent RFI in URLLCs scenarios. One-dimensional network construction is also exploited to improve the accuracy of the proposed SSL algorithm. On the basis of the proposed RFI framework, we analyze the overall uplink transmission error probability and network availability of URLLCs with massive MIMO, which can achieve comparable symmetry performance with that of downlink, and experimental evaluation is also provided to gain comprehensive insight on RFI. Numerical and experimental results demonstrate the effectiveness of our proposed RFI framework and the impact of channel correlation, and provide design guidelines for supporting the radio frequency identification of URLLC applications in IIoT systems.

Reliability-optimal pilot-assisted transmission for URLLC over non-reciprocal MISO channels: TDD or FDD?

In this paper, we focus on the pilot-assisted transmission design for downlink URLLC over non-reciprocal channels, in which the multi-antenna controller sends mission-critical data signals to a single-antenna actuator. In this system, the prior knowledge of downlink channel state information (CSI) is a prerequisite for reliable data transmission. Generally, the acquisition of downlink CSI is completed either via the uplink pilot measurement exploiting channel reciprocity and time-division duplex (TDD) operation, or via the downlink pilot measurement with quantized feedback and frequency division duplex (FDD) operation. Inspired by this, we aim to investigate how the degree of channel non-reciprocity impacts the transmission reliability of our URLLC system, and the superiority between the TDD mode and FDD mode in terms of transmission reliability maximization. To describe the degree of reliability loss, we derive the closed-form approximations on the transmission error probability of URLLC in TDD and FDD modes, via leveraging the Gauss-Hermite and Gauss-Chebyshev quadrature rules. Following by the theoretical approximations, we demonstrate how to determine the optimal training pilot length and quantized feedback duration that maximize the transmission reliability under given latency constraint. Through numerical results, we validate the accuracy of theoretical approximations derived in this paper, and obtain some meaningful conclusions.

Performance of Multiuser Downlink Cell-Free Massive MIMO Systems With Hard Deadlines

Cell-free massive MIMO (mMIMO) is expected to be a novel network architecture to support massive connections, ultra-high transmission rate, and ultra-reliability low-latency transmission. Therefore, applications in ultra-reliable low-latency communications (URLLC) can be served by the cell-free mMIMO network in the future. This paper considers a multiuser downlink cell-free massive MIMO communication system and defines two events to evaluate the system’s failure in latency and reliability performance of the transmission. The first kind of event is defined as Transmission Error, where the value of the downlink rate becomes too high to be supported by the channel between the access points (APs) and user equipments (UEs), while the second event is defined as Time Overflow, where the time of packet delivery violates the hard deadline. The probabilities of these events can help evaluate the URLLC performance of the system because the probability of Transmission Error reflects the reliability of the transmission while the probability of Time Overflow reflects the latency of the transmission. We derive the expressions of the probabilities of these two events and the final probability system outage, with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding, based on the properties of Gamma distribution and the approximation for non-isotropic vectors. We finally provide the numerical results by system-level simulations to show that the expressions are accurate and discuss the influence of the noise variance, the number of UEs, and the precoding scheme on the URLLC performance.

Performance Analysis of an Artificial Intelligence Nanosystem with Biological Internet of Nano Things

Artificial intelligence (AI) has recently been used in nanomedical applications, in which implanted intelligent nanosystems inside the human body were used to diagnose and treat a variety of ailments with the help of the Internet of biological Nano Things (IoBNT). Biological circuit engineering or nanomaterial-based architectures can be used to approach the nanosystem. In nanomedical applications, the blood vascular medium serves as a communication channel, demonstrating a molecular communication system based on flow and diffusion. This paper presents a performance study of the channel capacity for flow-based-diffusive molecular communication nanosystems that takes into account the ligand-receptor binding mechanism. Unlike earlier studies, we take into account the effects of biological physical characteristics such as blood pressure, blood viscosity, and vascular diameter on channel capacity. Furthermore, in terms of drug transmission error probability, the inter-symbol interference (ISI) phenomenon is applied to the proposed system. The numerical results show that the proposed AI nanosystems-based IoBNT technology can be successfully implemented in future nanomedicine.

Effective Capacity Analysis of NOMA Networks with Short Packets

Low latency and a massive connection have become the requirements of energy internet wireless communication. Effective capacity analysis of non-orthogonal multiple access (NOMA) networks with short packets is of vital importance in energy internet communication planning and design. Low-latency communications are one of the main application scenarios in next-generation wireless networks. This paper focuses on the effective capacity of NOMA networks, where the finite blocklength, delay exponent, and transmission error probability are taken into account. New exact and asymptotic expressions of effective capacities are derived for arbitrarily ordered users with a finite blocklength. Based on the analytical results, the high Signal-to-Noise Ratio slopes of effective capacity in NOMA networks are carefully attained. The numerical results validate that (a) non-orthogonal users are capable of obtaining a larger effective capacity when the blocklength decreases, and that (b), as the value of the error probability and delay exponent increases, the effective capacity of non-orthogonal users worsens.

An Encryption-Aware PHY Security Framework for 4-Node Gaussian Wiretap Channels With Joint Power Constraint

In traditional physical layer security paradigm, no leakage of confidential information to the eavesdropper is tolerated regardless of whether the message is encrypted or not. This will result in an achievable secure transmission rate that is significantly smaller than the channel capacity. This article presents a novel approach that allows treatment of physical layer security in conjunction with encryption to achieve a flexible trade-off of system resources. We propose a novel framework to model the interplay between secured transmission rate and error probability in error prone ciphertexts. To this end, we use the concept of rate-equivocation region to establish such a connection. To clearly describe the application of our framework, we consider the case of 4-node Gaussian wiretap channel. For such a channel, we characterize the rate-equivocation region in different scenarios, and then use it to study the achievable rate of encryption-aware physical layer security. The obtained results show that, for a fixed transmission power, the prior knowledge of encryption can significantly increase the secured transmission rate. In addition, encryption-aware physical layer security can achieve a target transmission rate at a reduced transmission power compared to the conventional encryption-agnostic physical layer security.

Combining Non-orthogonal Transmission with Network-Coded Cooperation: Performance Analysis Over Nakagami-m Fading Channels

This paper investigates efficient transmission design in a class of multi-user cooperation networks, in which multiple information sources intend to distribute their messages to a sufficient proportion of ambient destinations with the assistance of multiple relays. We apply a relaying scheme that combines non-orthogonal transmission with network coding techniques to balance channel consumption and inter-user interference. The sources and relays are divided into clusters, terminals within each of which are allowed to non-orthogonally access the same channel. A class of finite-field network codes are adopted in the relays. We provide the methods to derive the system transmission error probability and diversity-multiplexing tradeoff (DMT), over Nakagami- $m$ fading channels. Through error probability and finite-SNR DMT analysis for certain clustering strategies, and infinite-SNR DMT analysis for general situations, we show that the considered relaying scheme can notably improve system performance over the conventional approach that demands only orthogonal transmission in network-coded cooperation networks.

Fog computing in health: A systematic literature review

Currently, technology greatly benefits the area of healthcare. Modern computers can quickly process a large volume of patient health records. Due to recent advances in the area of Internet of Things and healthcare, patient data can be dispersed in multiple locations. As a result, scientists have been proposing solutions based on Cloud Computing to manage healthcare data. However, suchs solutions present challenges regarding access latency, context-awareness, and large volumes of data. There is an increased probability of processing and transmission errors are more likely to occur as health data sets become larger and more complex. In this context, Fog Computing presents itself as an alternative to reduce health data management complexity, consequently increasing its reliability. To that end, it is important to comprehend the associated challenges before defining a Fog Computing-based architecture to manage healthcare data. This article presents a systematic literature review of fog computing being applied to healthcare area. We propose a taxonomy to explore the open issues and most important challenges on these fields of study. We selected 1070 scientific articles published in the last 10 years, filtering the 44 most significant works for an in-depth analysis. We found that there is several challenges to be addressed such as interoperability, privacy, security, data processing, management of resources and Big Data issues. Also, our contribution include developing a taxonomy for the Fog Computing and healthcare fields of study and finding out challenges and open questions of this area.

MODELLING OF SPINTRONIC DEVICES FOR APPLICATION IN RANDOM ACCESS MEMORY

The article analyzes the physical processes that occur in spin-valve structures during recording process which occurs in high-speed magnetic memory devices. Considered are devices using magnetization of the ferromagnetic layer through transmitting magnetic moment by polarized spin (STT-MRAM). Basic equations are derived to model the information recording process in the model of symmetric binary channel. Because the error probability arises from the magnetization process, a model of the magnetization process is formed, which is derived from the Landau-Lifshitz-Gilbert equations under the assumption of a single-domain magnet. The choice of a single-domain model is due to the nanometer size of the flat magnetic layer. The developed method of modeling the recording process determines the dependence of such characteristics as the bit error probability and the rate of recording on two important technological characteristics of the recording process: the value of the current and its duration. The end result and the aim of the simulation is to determine the optimal values of the current and its duration at which the speed of the recording process is the highest for a given level of error probability. The numerical values of the transmission rate and error probability were obtained for a wide range of current values (10–1500 μA) and recording time of one bit (1–70 ns), and generally correctly describe the process of information transmission. The calculated data were compared with the technical characteristics of existing industrial devices and devices which are the object of the scientific research. The resulting model can be used to simulate devices using different values of recording currents: STT-MRAM series chips using low current values (500-100 μA), devices in the stage of technological design and using medium current values (100–500 μA) and devices that are the object of experimental scientific research and use high currents (500–1000 μA). The model can also be applied to simulate devices with different data rates, which have different requirements for both transmission speed and bit error probability. In this way, the model can be applied to both high-speed memory devices in computer systems and signal sensors, which are connected to sensor networks or connected to the IoT.

On the Ultra-Reliable and Low-Latency Communications for Tactile Internet in 5G Era

Abstract New generations of mobile telephony succeed every decade, each bringing an evolution or even a revolution. Nowadays, the Internet of Things and the tactile Internet are starting to grow, and 5G technology is there to enable these services. 5G technology has introduced three types of services, namely eMBB (for services requiring very high bit rates), mMTC (for massive connection of user equipment), and uRLLC (for critical services requiring very high reliability and extremely reduced latency). In this paper, we have dealt with some issues encountered by uRLLC services for tactile Internet services. In this article, we have studied the transmission of very small packets as required by the 5G uRLLC services. We also examined the probability of transmission error and its variation concerning the transmission delay and the length of the packet transmitted. This study was conducted considering its application in the Tactile Internet.

Transmission Performance Optimization for URLLC With Limited Training and Feedback Overheads

Pilot-assisted channel training for acquiring the channel state information (CSI) is a very critical functionality in URLLC to ensure reliable data transmission. In this paper, we consider the joint uplink and downlink URLLC transmission in which the source uploads a packet to the BS, and then BS forwards this packet to the destination. CSI in the uplink can be estimated at BS by sending a training pilot from source while for the downlink, an extra codebook-based channel quantization is required at destination to measure the estimated channel and feedback the limited-precision results to BS for beamforming before data transmission. This could bring about channel estimation errors, quantization errors and decoding errors, respectively caused by limited training symbols, limited feedback overheads and finite blocklength. In view of this, we for the first time investigate the transmission performance of URLLC under this circumstance and design a performance optimization framework to maximize the transmission reliability. Particularly, the impact of channel estimation accuracy on the transmission reliability of URLLC is studied based on the finite blocklength information theory. An analytical closed-form expression of transmission error probability under imperfect CSI is also derived with respect to key parameters, like the training pilot length, feedback bits and data transmission durations. By jointly optimizing those parameters, we could derive the maximum transmission reliability. Simulation results are provided to validate the effectiveness of our proposed performance optimization framework.