An Overview of QoS Enhancements for Wireless Vehicular Networks

Vehicular ad hoc networks (VANETs) allow vehicles to form a self-organized network without the need for permanent infrastructure. Even though VANETs are mobile ad hoc networks (MANETs), because of the intrinsic characteristics of VANETs, several protocols designed for MANETs cannot be directly applied for VANETs. With high number of nodes and mobility, ensuring the Quality of Service (QoS) in VANET is a challenging task. QoS is essential to improve the communication efficiency in vehicular networks. Thus a study of QoS in VANET is useful as a fundamental for constructing an effective vehicular network. In this paper, we present a timeline of the development of the existing protocols for VANETs that try to support QoS. Moreover, we classify and characterize the existing QoS protocols for VANETs in a layered perspective. The review helps in understanding the strengths and weaknesses of the existing QoS protocols and also throws light on open issues that remain to be addressed. Keywords: QoS, VANET, Inter-Vehicle Communications, MAC, Routin

Transmission protocols in Cognitive Radio Mesh Networks

A Cognitive Radio (CR) is a radio that can adjust its transmission limit based on available spectrum in its operational surroundings. Cognitive Radio Network (CRN) is made up of both the licensed users and unlicensed users with CR enable and disabled radios. CR’S supports to access dynamic spectrum and supports secondary user to access underutilized spectrum efficiently, which was allocated to primary users. In CRN’S most of the research was done on spectrum allocation, spectrum sensing and spectrum sharing. In this literature, we present various Medium Access (MAC) protocols of CRN’S. This study would provide an excellent study of MAC strategies

Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks

The problem of distributed rate maximization in multi-channel ALOHA networks is considered. First, we study the problem of constrained distributed rate maximization, where user rates are subject to total transmission probability constraints. We propose a best-response algorithm, where each user updates its strategy to increase its rate according to the channel state information and the current channel utilization. We prove the convergence of the algorithm to a Nash equilibrium in both homogeneous and heterogeneous networks using the theory of potential games. The performance of the best-response dynamic is analyzed and compared to a simple transmission scheme, where users transmit over the channel with the highest collision-free utility. Then, we consider the case where users are not restricted by transmission probability constraints. Distributed rate maximization under uncertainty is considered to achieve both efficiency and fairness among users. We propose a distributed scheme where users adjust their transmission probability to maximize their rates according to the current network state, while maintaining the desired load on the channels. We show that our approach plays an important role in achieving the Nash bargaining solution among users. Sequential and parallel algorithms are proposed to achieve the target solution in a distributed manner. The efficiencies of the algorithms are demonstrated through both theoretical and simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM Transactions on Networking, part of this work was presented at IEEE CAMSAP 201

Analysis and experimental verification of frequency-based interference avoidance mechanisms in IEEE 802.15.4

More and more wireless networks are deployed with overlapping coverage. Especially in the unlicensed bands, we see an increasing density of heterogeneous solutions, with very diverse technologies and application requirements. As a consequence, interference from heterogeneous sources-also called cross-technology interference-is a major problem causing an increase of packet error rate (PER) and decrease of quality of service (QoS), possibly leading to application failure. This issue is apparent, for example, when an IEEE 802.15.4 wireless sensor network coexists with an IEEE 802.11 wireless LAN, which is the focus of this work. One way to alleviate cross-technology interference is to avoid it in the frequency domain by selecting different channels. Different multichannel protocols suitable for frequency-domain interference avoidance have already been proposed in the literature. However, most of these protocols have only been investigated from the perspective of intratechnology interference. Within this work, we create an objective comparison of different candidate channel selection mechanisms based on a new multichannel protocol taxonomy using measurements in a real-life testbed. We assess different metrics for the most suitable mechanism using the same set of measurements as in the comparison study. Finally, we verify the operation of the best channel selection metric in a proof-of-concept implementation running on the testbed

Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

This paper investigates the use of deep reinforcement learning (DRL) in a MAC protocol for heterogeneous wireless networking referred to as Deep-reinforcement Learning Multiple Access (DLMA). The thrust of this work is partially inspired by the vision of DARPA SC2, a 3-year competition whereby competitors are to come up with a clean-slate design that "best share spectrum with any network(s), in any environment, without prior knowledge, leveraging on machine-learning technique". Specifically, this paper considers the problem of sharing time slots among a multiple of time-slotted networks that adopt different MAC protocols. One of the MAC protocols is DLMA. The other two are TDMA and ALOHA. The nodes operating DLMA do not know that the other two MAC protocols are TDMA and ALOHA. Yet, by a series of observations of the environment, its own actions, and the resulting rewards, a DLMA node can learn an optimal MAC strategy to coexist harmoniously with the TDMA and ALOHA nodes according to a specified objective (e.g., the objective could be the sum throughput of all networks, or a general alpha-fairness objective)