Addressing Outdoor Throughput Sensitivity for Mobile Vehicles by Enhancing the VMESH MAC Protocol

To provide safe and efficient transportation, Vehicular Ad-Hoc Networks (VANETs) allow for the communication between a vehicle to another vehicle and for the communication between vehicles and stations near the road. As autonomous vehicles become closer to commercializing, the ability for moving vehicles to quickly and successfully send and receive packets becomes increasingly important. In this thesis, the 802.11p WAVE MAC protocol which was created specifically to address Vehicular Ad-Hoc Networks (VANETs), was analyzed. After reviewing existing models used to enhance throughput, the VMESH protocol was found to be better than the legacy WAVE MAC protocol. However, the VMESH protocol's channel allocation contention resolving scheme leads to a decreased throughput. This thesis proposes a new channel allocation scheme, Linear Modulus Autonomous Ordering (LMAO), that allows maximum channel utilization and therefore, an increased throughput. Given the number of cars in a system, the number of channels in a system, and the range of neighbors a car can see, the LMAO channel allocation methodology is found to perform significantly better than the VMESH and an upper bound approximated WAVE MAC channel allocation method

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Timed power line data communication

With the ever increasing demand for data communication methods, power line communication has become an interesting alternative method for data communication. Power line communication falls into two categories: one for data transmission between sites in the power grid and the other for home or office networking. When considering home or office networking, existing methods are either too slow for tasks other than simple automation, or are very fast with a higher cost than necessary for the desired function. The objective in this work is to develop a lower cost communication system with an intermediate data transmission rate.At first glance, power line communication looks like a good option because of the availability of power outlets in every room of a building. However, the power conductors were installed solely for the purpose of distributing 60 Hz mains power and, for data signals, they exhibit very high attenuation, variable impedance and there is radio frequency shielding. Furthermore, many of the 60 Hz loads produce radio frequency interference that impedes data communication. Previous research has shown that much of the noise is time synchronous with the 60 Hz mains frequency and that the majority of data errors occur during these periods of high noise. This work develops a power line communication protocol that coordinates transmissions and uses only the predictable times of lower noise. Using a central control strategy, the power line 60 Hz mains signal is divided into 16 timeslots and each timeslot is monitored for errors. The central controller periodically polls all stations to learn which timeslots have low noise and it then controls all transmissions to make the best use of these good timeslots. The periodic polling allows the system to adapt to changes in electrical loading and noise. This control strategy has been achieved with modest complexity and laboratory measurements have shown throughput approaching 70% of the modem bit rate

Distributed detection, localization, and estimation in time-critical wireless sensor networks

In this thesis the problem of distributed detection, localization, and estimation (DDLE) of a stationary target in a fusion center (FC) based wireless sensor network (WSN) is considered. The communication process is subject to time-critical operation, restricted power and bandwidth (BW) resources operating over a shared communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm is proposed to solve the DDLE problem consisting of two dependent stages: distributed detection and distributed estimation. The WSN performs distributed detection first and based on the global detection decision the distributed estimation stage is performed. The communication between the SNs and the FC occurs over a shared channel via a slotted Aloha MAC protocol to conserve BW. In distributed detection, hard decision fusion is adopted, using the counting rule (CR), and sensor censoring in order to save power and BW. The effect of Rayleigh fading on distributed detection is also considered and accounted for by using distributed diversity combining techniques where the diversity combining is among the sensor nodes (SNs) in lieu of having the processing done at the FC. Two distributed techniques are proposed: the distributed maximum ratio combining (dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show superior detection performance when compared to conventional diversity combining procedures that take place at the FC. In distributed estimation, the segmented distributed localization and estimation (SDLE) framework is proposed. The SDLE enables efficient power and BW processing. The SOLE hinges on the idea of introducing intermediate parameters that are estimated locally by the SNs and transmitted to the FC instead of the actual measurements. This concept decouples the main problem into a simpler set of local estimation problems solved at the SNs and a global estimation problem solved at the FC. Two algorithms are proposed for solving the local problem: a nonlinear least squares (NLS) algorithm using the variable projection (VP) method and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS), NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm combination delivers the best localization and estimation performance. In fact , the target can be localized with less than one meter error. The SNs send their local estimates to the FC over a shared channel using the slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel. However, Aloha is known for its relatively high medium access or contention delay given the medium access probability is poorly chosen. This fact significantly hinders the time-critical operation of the system. Hence, multi-packet reception (MPR) is used with slotted Aloha protocol, in which several channels are used for contention. The contention delay is analyzed for slotted Aloha with and without MPR. More specifically, the mean and variance have been analytically computed and the contention delay distribution is approximated. Having theoretical expressions for the contention delay statistics enables optimizing both the medium access probability and the number of MPR channels in order to strike a trade-off between delay performance and complexity

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial