Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrow band IoT (NB-IoT) as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This study further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142% respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of -20∘C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, RPMA) and 48% in LTE-M compared with environmental temperature of 40C

Energy-Per-Bit Performance Analysis of Relay-Assisted Power Line Communication Systems

This paper provides a comprehensive analysis of the energy efficiency performance for different relaying schemes over the non-Gaussian power line communication (PLC) channel. Specifically, amplify-and-forward (AF), decode-and-forward (DF), selective DF (SDF) and incremental DF (IDF) relaying systems are investigated. For a more realistic scenario, the power consumption profile of the PLC modems is assumed to consist of both dynamic and static power. For each system, we derive accurate analytical expressions for the outage probability and the minimum energy-per-bit performance. For the sake of comparison and completeness as well as to quantify the achievable gains, we also analyze the performance of a single-hop PLC system. Monte Carlo simulations are provided throughout this paper to validate the theoretical analysis. Results reveal that AF relaying over the non-Gaussian PLC channel does not always enhance the performance and that the IDF PLC system offers the best performance compared to all other schemes considered. It is also shown that increasing the channel variance, which is related to the PLC network branching, and impulsive noise probability can considerably deteriorate the system performance. Furthermore, when the end-to-end distance is relatively small, it is found that the single-hop PLC approach can perform better than AF relaying

Optimization of Impulsive Noise Mitigation Scheme for PAPR Reduced OFDM Signals Over Powerline Channels

The IEEE 1901 powerline standard can be deployed using orthogonal frequency division multiplexing (OFDM) since it is robust over impulsive channels. However, the powerline channel picks up impulsive interference that the conventional OFDM driver cannot combat. Since the probability density function (PDF) of OFDM amplitudes follow the Rayleigh distribution, it becomes difficult to correctly predict the existence of impulsive noise (IN) in powerline systems. In this study, we use companding transforms to convert the PDF of the conventional OFDM system to a uniform distribution which avails the identification and mitigation of IN. Results show significant improvement in the output signal-to-noise ratio (SNR) when nonlinear optimization search is applied. We also show that the conventional PDF leads to false IN detection which diminishes the output SNR when nonlinear memoryless mitigation scheme such as clipping or blanking is applied. Thus, companding OFDM signals before transmission helps to correctly predict the optimal blanking or clipping threshold which in turn improves the output SNR performance

On Companding and Optimization of OFDM Signals for Mitigating Impulsive Noise in Power-line Communication Systems

Generally, the probability density function (PDF) of orthogonal frequency division multiplexing (OFDM) signal amplitudes follow the Rayleigh distribution, thus, it is difficult to correctly predict the existence of impulsive noise (IN) in powerline communication (PLC) systems. Compressing and expanding the amplitudes of some of these OFDM signals, usually referred to as companding, is a peak-to-average power ratio (PAPR) reduction technique that distorts the amplitudes of OFDM signals towards a uniform distribution. We suggest its application in PLC systems such as IEEE 1901 powerline standard (which uses OFDM) to reduce the impacts of IN. This is because the PLC channel picks up impulsive interference that the conventional OFDM driver cannot combat. We explore, therefore, five widely used companding schemes that convert the OFDM signal amplitude distribution to uniform distribution to avail the mitigation of IN in PLC system receivers by blanking, clipping and their hybrid (clipping-blanking). We also apply nonlinear optimization search to find the optimal mitigation thresholds and results show significant improvement in the output signal-to-noise ratio (SNR) for all companding transforms considered of up to 4 dB SNR gain. It follows that the conventional PDF leads to false IN detection which diminishes the output SNR when any of the above three nonlinear memoryless mitigation schemes is applied

A new self-planning methodology based on signal quality and user traffic in Wi-Fi networks

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-19909-8_2Wi-Fi networks have become one of the most popular technologies for the provisioning of multimedia services. Due to the exponential increase in the number of Access Points (AP) in these networks, the automation of the planning, configuration, optimization and management tasks has become of prime importance. The efficiency of these automated processes can be improved with the inclusion of data analytics mechanisms able to process the large amount of data that can be collected from Wi-Fi networks by powerful monitoring systems. This paper presents a new self-planning methodology that collects historical network measurements and extracts knowledge about user signal quality and traffic demands to determine adequate AP relocations. The performance of the proposed AP relocation methodology based on a genetic algorithm is validated in a real Wi-Fi network. The proposed approach can be easily adapted to other contexts such as small cell networks.Peer ReviewedPostprint (author's final draft

An Efficient Caching and Offloading Resource Allocation Strategy in Vehicular Social Networks

Limited edge server resources and uneven distribution of traffic density in vehicular networks result in problems such as unbalanced network load and high task processing latency. To address these issues, we proposed an efficient caching and offloading resource allocation (ECORA) strategy in vehicular social networks. First, to improve the utilization of vehicular idle resources, a collaborative computation and storage resource allocation mechanism was designed using mobile social similarity. Next, with the optimization objective of minimizing the average task processing delay, we studied the combined resource allocation optimization problem and decoupled it into two sub-problems. For the service caching subproblem, we designed a stable matching algorithm by mobile social connections to dynamically update the cache resource allocation scheme for improving the task unloading efficiency. For the task offloading subproblem, a discrete cuckoo search algorithm based on differential evolution was designed to adaptively select the best task offloading scheme, which minimized the average task processing delay. Simulation results revealed that the ECORA strategy outperformed the resource allocation strategy based on particle swarm optimization and genetic algorithm, and reduced the average task processing delay by at least 7.59%. Meanwhile, the ECORA strategy can achieve superior network load balancing

A Comparison between Orthogonal and Non-Orthogonal Multiple Access in Cooperative Relaying Power Line Communication Systems

Most, if not all, existing studies on power line communication (PLC) systems as well as industrial PLC standards are based on orthogonal multiple access schemes such as orthogonal frequency-division multiplexing and code-division multiple access. In this paper, we propose non-orthogonal multiple access (NOMA) for decode-and-forward cooperative relaying PLC systems to achieve higher throughput and improve user fairness. To quantitatively characterize the proposed system performance, we also study conventional cooperative relaying (CCR) PLC systems. We evaluate the performance of the two systems in terms of the average capacity. In this respect, accurate analytical expressions for the average capacity are derived and validated with Monte Carlo simulations. The impact of several system parameters such as the branching, impulsive noise probability, cable lengths, the power allocation coefficients and input signal-to-noise ratio are investigated. The results reveal that the performance of the proposed NOMA-PLC scheme is superior compared to that of the CCR-PLC system. It is also shown that NOMA-PLC can be more effective in reducing electromagnetic compatibility associated with PLC and that increasing the network branches can considerably degrade performance. Moreover, optimizing the power allocation coefficients is found to be of utmost importance to maximize the performance of the proposed system

A Lightweight Decentralized-Learning-Based Automatic Modulation Classification Method for Resource-Constrained Edge Devices

Due to the computing capability and memory limitations, it is difficult to apply the traditional deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic modulation classification (AMC). Recently, many works attempt to use different ways to realize lightweight AMC methods for EDs. However, the lightweight seems to be a contradiction with the classification performance in these lightweight networks. In this article, we propose an efficient lightweight decentralized-learning-based AMC (DecentAMC) method using spatiotemporal hybrid deep neural network based on multichannels and multifunction blocks (MCMBNN). Specifically, the lightweight network is designed from the perspectives of comprehensive consideration of lightweight and classification performance, which is composed of three parts to extract different features for realizing high classification performance and they are phase estimator and transformer (PET) block, spatial feature extraction block and temporal feature extraction & Softmax block. In addition, we use a multichannel input to extract complementary features of different channels for a better classification performance. The proposed DecentAMC method is an efficient training method, which is achieved by the cooperation in which multiple EDs update and upload the model weight to a central device (CD) for model aggregation to avoid the data privacy disclosure and reduce the computing power and storage pressure of CD. Experimental results show that the proposed MCMBNN can obtain an improved classification accuracy while reducing model complexity with the contributions of three blocks. Moreover, the proposed DecentAMC method can be deployed on EDs efficiently. Thus, the method has the advantages of avoiding data leakage on EDs and relieving the computing pressure of CD with relatively lower communication overhead. The simulation code and datasets are shared on GitHub