increasing efficiency of resource allocation for d2d communication in nb iot context

Abstract Internet of things (IoT) and device to device (D2D) communications are among the novel promising technologies in the current releases of 4G and they will play a fundamental role in the next generation 5G as well. In this paper, it is investigated the impact of allocation strategies that take into account the mutual interference in D2D Narrow-Band IoT terminals and cellular terminals transmitting in the same resource block. In a multi-cellular downlink context, the proposed approach and the analysis can serve also as an efficient criterion for selecting the target SINR, useful for managing the power control in the uplink. The rate improvement, measured with the proposed approach, is between 10% and 15% w.r.t. conventional techniques

Rate-latency optimization for NB-IoT with adaptive resource unit configuration in uplink transmission

Narrowband Internet of Things (NB-IoT) is a cellular IoT communication technology standardized by 3rd Generation Partnership Project (3GPP) for supporting massive machine type communication and its deployment can be realized by a simple firmware upgrade on existing long term evolution (LTE) networks. The NB-IoT requirements in terms of energy efficiency, achievable rates, latency, extended coverage, make the resource allocation, in a limited bandwidth, even a more challenging problem w.r.t. to legacy LTE. The allocation, done with subcarrier (SC) granularity in NB-IoT, should maintain adequate performance for the devices while keeping the power consumption as low as possible. Nevertheless, the optimal solution of the resource allocation problem is typically unfeasible since nonconvex, NP-hard and combinatorial because of the use of binary variables. In this article, after the formulation of the optimization problem, we study the resource allocation approach for NB-IoT networks aiming to analyze the tradeoff between rate and latency. The proposed suboptimal algorithm allocates radio resource (i.e., SCs) and transmission power to the NB-IoT devices for the uplink transmission and the performance is compared in terms of latency, rate, and power. By comparing the proposed allocation to a conventional round robin (RR) and to a brute-force approach, we can observe the advantages of the formulated allocation problem and the limited loss of the suboptimal solution. The proposed algorithm outperforms the RR by a factor 2 in terms of spectral efficiency and, moreover, the study includes techniques that reduce the dropped packets from 29% to 1.6%

Hybrid power control for multi-carrier systems

Uplink conventional power control techniques in systems with frequency reuse one have some limitations, typically regarding the tradeoff between the average throughput of users at cell center and cell edge. Moreover, one of the main drawbacks is the excess use of power. From our point of view, one of the reasons for this waste of power is the practice of giving all the users in the cell the same target SINR in the power control process. Since there exists a rate limit for users in the cell, depending on their positions, a Hybrid Power Control (HPC) technique is introduced here in order to overcome these limitations and provide a more flexible solution. The HPC uses power control with at least two different types of setup and the rate limit of the cell users is respected. Moreover, this HPC method shows a significant reduction in the average cell transmission power, satisfactory cell edge users performance and an improvement in the overall cell rate, around 25%. Moreover, the average transmitted power is reduced by more than 20 dB

On the Kavya–Manoharan–Burr X Model: Estimations under Ranked Set Sampling and Applications

A new two-parameter model is proposed using the Kavya–Manoharan (KM) transformation family and Burr X (BX) distribution. The new model is called the Kavya–Manoharan–Burr X (KMBX) model. The statistical properties are obtained, involving the quantile (QU) function, moment (MOs), incomplete MOs, conditional MOs, MO-generating function, and entropy. Based on simple random sampling (SiRS) and ranked set sampling (RaSS), the model parameters are estimated via the maximum likelihood (MLL) method. A simulation experiment is used to compare these estimators based on the bias (BI), mean square error (MSER), and efficiency. The estimates conducted using RaSS tend to be more efficient than the estimates based on SiRS. The importance and applicability of the KMBX model are demonstrated using three different data sets. Some of the useful actuarial risk measures, such as the value at risk and conditional value at risk, are discussed

Energy efficiency and latency optimization for IoT URLLC and mMTC use cases

The Internet of Things (IoT) is an essential part of 5G, Beyond-5G (B5G), and 6G systems; it has several applications in two of the principal 5G use cases, namely ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC), and in their successors within B5G and 6G: extreme ultra-reliable low-latency communication (eURLLC) and ultra-massive machine-type communication (umMTC). IoT systems, which are characterized by narrow bandwidths, have stringent requirements owing to the specific nature of their applications and use cases. The purpose of this study is to investigate and jointly optimize the energy efficiency (EE) and latency through resource allocation for IoT cellular systems. With regard to the contributions, in this study we investigated the optimization of EE in narrowband IoT systems, compared resource unit configurations (RUCs), jointly formulated the optimization of EE and latency, and introduced a suboptimal but efficient algorithm. More precisely, as the EE performance of various resource unit configurations has not been exhaustively investigated in the current state of the art, we analyzed and compared the EE of RUCs. The results show vast differences in performance between RUCs. For example, in terms of EE, the best RUC has an EE more than 80 times higher than the worst, which illustrates the importance of this investigation. We then proposed a scheduler based on the shortest job first (SJF) for minimum latency allocation, and another scheduler based on a joint evaluation of EE and latency. With respect to conventional techniques, these schedulers achieve a better trade-off between latency reduction and gain in terms of EE for a wider range of parameter configurations in multi-cellular layouts. The study demonstrates that in the presence of repetitions, algorithms that achieve high EE will mostly achieve low latency

Different Estimation Methods for New Probability Distribution Approach Based on Environmental and Medical Data

In this article, we introduce a new extension of the power Lomax (PLo) model by combining the type II exponentiated half-logistic class of statistical models and the PLo model. The new suggested statistical model called type II exponentiated half-logistic-PLo (TIIEHL-PLo) model. However, the new TIIEHL-PLo model is more flexible and applicable than the PLo model and some extensions of THE PLo model, especially those in environmental and medical fields. Some general statistical properties of the TIIEHL-PLo model are computed. Six different estimation approaches, namely maximum likelihood (ML), least-square (LS), weighted least-squares (WLS), maximum product spacing (MPS), Cramér–von Mises (CVM), and Anderson–Darling (AD) estimation approaches, are utilized to estimate the parameters of the TIIEHL-PLo model. The simulation experiment examines the accuracy of the model parameters by employing six different methodologies of estimation. In this study, we analyze three real datasets from the environmental and medical fields to highlight the relevance and adaptability of the proposed approach. The newly suggested model is exceptionally adaptable and outperforms several well-known statistical models