Terms Of Average Data Rate Research Articles

RL-IoT: Reinforcement Learning-Based Routing Approach for Cognitive Radio-Enabled IoT Communications

Internet of Things (IoT) devices are widely being used in various smart applications and being equipped with cognitive radio (CR) capabilities for dynamic spectrum allocation. Our objectives in this work are to achieve higher data rates and minimize end-to-end routing delays in CR-enabled IoT communication in order to maximize throughput. We propose a reinforcement learning (RL)-based routing approach in the cognitive radio network (CRN)-based IoT environment. The idea is to add the channel selection decision capability to the network layer in order to minimize packet collisions as well as end-to-end delay (EED). We perform a comprehensive performance evaluation of the proposed RL-IoT routing mechanism by simulating the cognitive radio-enabled Internet of Things (CR-IoT) communication environment in the cognitive radio cognitive network (CRCN) simulator and comparing the network performance achieved by our proposed mechanism with that of the recent AODV-based routing mechanism for IoT (AODV-IoT), ELD-CRN, and SpEED-IoT routing approaches. Our evaluation results show that the RL-IoT model performs better than existing approaches in terms of average data rate, throughput, packet collision, and EED.

Delay-Aware Admission Control and Beam Allocation for 5G Functional Split Enhanced Millimeter Wave Wireless Fronthaul Networks

In this paper, we study a delay-aware admission control (AC) and beam allocation (BA) problem with the consideration of quality of experience (QoE) and dynamic variation of channel condition for centralized unit (CU) and distributed unit (DU) based functional split options (FSOs) in millimeter wave (mmWave) fronthaul downlink networks. The original optimization problem aims to maximize time-average QoE subject to delay and queue stability constraints. The intractability of the considered problem comes from the necessity to allocate resources across periods of time slots. After applying virtual queue transformation and Lyapunov optimization method, the targeting problem can be converted into two independent AC and BA sub-problems in each time slot. The converted problem involves actual and virtual queues, which causes conflicting tendency. With the design of a flexible Lyapunov function, the influences of actual and virtual queues are theoretically to be proved balanced. Moreover, the AC sub-problem can be solved by Karush-Kuhn-Tucker condition, whereas mmWave BA sub-problem is tackled by genetic algorithm and matching game for the CU-based and DU-based FSOs, respectively. Simulation results demonstrate the applicability of the proposed CU-/DU-based algorithms in terms of average data rate, admission control rate, queue length, and corresponding system delay under either uniform or dense deployment of DUs. Moreover, the proposed scheme achieves the lowest delay and highest QoE performances while sustaining the system stability compared with the state-of-the-art mechanisms in open literatures.

Sequential Load Balancing for Link Aggregation Enabled Heterogeneous LiFi WiFi Network

Light fidelity (LiFi) is one of the promising communication technology for 6G internet of everything (IoE), however, it requires a line-of-sight (LoS) link; in contrast, WiFi can support moderate data rates even in the absence of LoS. As the electromagnetic spectrums of LiFi and WiFi do not overlap, these technologies can be used for concurrent communication, thus, resulting into a link aggregation (LA) enabled heterogeneous LiFi WiFi networks (HLWN). However, for the most efficient utilization of a LA enabled HLWN, proper load balancing is essential. Therefore, in this paper, we propose a novel sequential load balancing method with reinforcement learning (RL)-based access point (AP) assignment followed by optimum resource allocation for LA enabled HLWN. It is observed that the proposed method outperforms the baseline received signal strength (RSS) scheme by around 37% and 56% in terms of average data rate and user satisfaction, respectively. Furthermore, the proposed method performance closely matches to the exhaustive search with the added advantage of reasonably low complexity. Additionally, the robustness of the proposed method is proven by considering two different user’s mobility models in this work.

Hierarchical Maritime Radio Networks for Internet of Maritime Things

Despite the potential benefits of a maritime Internet of Things, a number of issues hinder its realization, including the need for wide area coverage and cost-effectiveness. Toward these needs, a model is first presented for uplink maritime radio communications that hierarchically employ Wi-Fi and cellular links for data transmission from marine user equipments (MUEs) on a ship to a cellular base station with distributed antennas (DAs) along a coastline. Then, the performances are evaluated given in terms of average data rate and outage probability in the hierarchical maritime radio networks. When evaluating the performances, heterogeneous channel characteristics of the hierarchical networks are taken into account in which Rayleigh fading is considered for the MUE-ship links, whereas shadowed-Rician fading is considered for the ship-DA links. Moreover, the effects due to various transmission errors that can be occurred at different communication layers are reflected in determining the quality of service (QoS) requirements of a ship. With the help of authors' analysis, an antenna selection algorithm is proposed under which the minimum size of a service cloud can be found to support the QoS requirements of MUEs with a ship. The numerical evaluations demonstrate the validity of the proposed algorithm.

Dynamic Shared Spectrum Allocation for Underlaying Device-to-Device Communications

This article provides an overview on spectrum sharing in D2D underlaying communications for 5G and beyond 5G applications. Various spectrum sharing algorithms are summarized within a framework of underlaying D2D communications in cellular networks to increase spectrum efficiency. Dynamic spectrum sharing algorithms in the frequency, power, and spatial dimensions are proposed for underlaying D2D communications with both single antenna and multiple antennas at the base station. Performance evaluations show the effectiveness of the proposed algorithms in terms of average data rate per D2D pair.

Online Power and Time Allocation in MIMO Uplink Transmissions Powered by RF Wireless Energy Transfer

Wireless energy transfer (WET) has been a promising technology to tackle the lifetime bottlenecks of energy-limited wireless devices in recent years. In this paper, we study a WET-enabled multiple-input multiple-output system including a base station (BS) and a user equipment (UE), which has a finite battery capacity. We consider slotted transmissions, where each slot includes two phases, namely, a downlink (DL) WET phase and an uplink (UL) wireless information transmission (WIT) phase. In the WET phase (a fraction $\tau$ of a slot), the BS transfers energy and the UE stores the received energy in the battery. In the WIT phase (a fraction $1-\tau$ of a slot), the UE transmits information to the BS by using the energy in the battery. Considering the power sensitivity $\alpha$ of the radio frequency to DC conversion circuits, the BS transfers energy only if the UE received power is larger than $\alpha$ , and the DL WET is formulated as a Bernoulli process. Based on the formulation, we propose an online power and time allocation algorithm to maximize the average data rate of UL WIT. We also extend the proposed algorithm to multiple user systems. The numerical results show that the proposed algorithm outperforms the existing schemes in terms of average data rate, energy efficiency, and outage probability.

Resource allocation based on cross-layer QoS-guaranteed scheduling for multi-service multi-user MIMO–OFDMA systems

Cross-layer strategies for resource allocation in wireless networks are essential to guaranty an efficient utilization of the scarce resource. In this paper, we present an efficient radio resource allocation scheme based on PHY/MAC cross layer design and QoS-guaranteed scheduling for multi-user (MU), multi-service (MS), multi-input multi-output (MIMO) concept, orthogonal frequency division multiple access (OFDMA) systems. It is about a downlink multimedia transmission chain in which the available resources as power and bandwidth, are dynamically allocated according to the system parameters. Among these parameters, we can mention the physical link elements such as channel state information, spectral efficiency and error code corrector rate, and MAC link variables, which correspond to the users QoS requirements and the queue status. Primarily, we use a jointly method which parametrizes these system parameters, according to the total power, and the bit error rate constraints. Secondly, we propose a QoS-guaranteed scheduling that shares the sub-carriers to the users. These users request several type of traffic under throughput threshold constraints. The main objective in this work is to adjust the average throughput per service of each user, according to their needs and likewise to satisfy a great number of connexions. Subsequently, we consider a model of moderated compartmentalization between various classes of services by partitioning the total bandwidth into several parts. Each class of service will occupy a part of the bandwidth and will be transmitted over a maximum number of sub-carriers. The simulation results show that the proposed strategy provides a more interesting performance improvement (in terms of average data rate and user satisfaction) than other existing resource allocation schemes, such as nonadaptive resource allocation strategy. The performances are also analyzed and compared for the two multi-service multi-user MIMO---OFDMA systems; with sub-carriers partitioning and without sub-carriers partitioning.

Random-Access Control Mechanisms Using Adaptive Traffic Load in ALOHA and CSMA Strategies for EDGE

In this paper, three random access control mechanisms based on the well-known Slotted ALOHA, NP-CSMA, and 1P-CSMA protocols are presented. The basic idea is to limit the number of transmissions and retransmissions at high traffic loads in order to minimize collisions while keeping system stability. A new medium-access control protocol called Adaptive Traffic Load (ATL) is proposed. With ATL, all users are assigned authorization of transmission probabilities that vary according to the prevailing average traffic conditions in the system. ATL ensures that the system throughput is kept constant at its maximum value regardless of the traffic load. A mathematical analysis to calculate the probability density function of the access delay in the ATL protocol under the assumption of infinite user population is also presented. Mean access delay follows increases exponentially with respect to the traffic load when conventional random access protocols are used. However, it follows a linear function with respect to the traffic load when ATL is used. The average traffic load of the system is an input of the ATL protocol in order to assign access authorization probabilities to all users attempting to access the network. A simple algorithm for traffic load estimation based on the probability of finding empty slots in the system within an estimation period is proposed in this paper to asses the average traffic load. In the numerical evaluations of ATL, the Enhanced Data for GSM Evolution (EDGE) system is considered as a case study. For high arrival rates, channel utilization can be low in EDGE even if the system has sufficient capacity to serve incoming data users. A mathematical analysis of ATL-Slotted ALOHA as well as ATL-CSMA is presented. In the case of ATL-CSMA, system throughput varies according to the cell size. Hence, the ATL protocol is evaluated in picocell, microcell and macrocell environments as recommended by ITU-R. Also, the performance of EDGE is evaluated in terms of average data rate and packet delay for both S-ALOHA and ATL S-ALOHA considering long range dependent traffic type.