Predictive decision-making for vehicular cognitive radio networks through Hidden Markov models

Abstract

Abstract-Vehicular networks that require additional spectrum for communication may leverage cognitive radio technology. Towards this aim, the vehicle must select one among several candidate channels for data transmission, with the possibility that other surrounding vehicles may also identify the same spectrum for their use. Such networks are distinguished from their classical, stationary counterparts by high mobility, timevarying and heterogeneous environment leading to dynamic channel availability. Owing to this changing environment, the history of spectrum usage information may be unavailable to the vehicles at all points on their journey, necessitating blind decisionmaking. In this paper, we propose a method of spectrum selection without a priori channel information. The main contributions of our work are: (i) we provide a framework to determine, through a Hidden Markov Model, whether a channel is occupied by a licensed user, another cognitive radio enabled vehicle, or if the observed signal fluctuations are due to noise; and (ii) we devise a prediction algorithm to determine which channels are likely to be available within the shortest amount of time. Our approach is verified through a simulation study, and we demonstrate equivalent performance to schemes that use prior history of the channel usage at specific vehicle locations.

Cognitive radio based connectivity management for resilient end-to-end communications in VANETs

“…In contrast, with regard to applications in the second category, (such as the cooperative forward collision warning, pre-crash sensing/warning, curve speed warning, left-turn assistance and hazardous location notification), communication reliability is a significant issue [3]. In recent years, a number of studies have investigated how to improve the quality of data delivery in VANETs, particularly in terms of throughput and latency, by making use of mobility prediction, routing, resource management and channel selection [21,22,23,24]. However, most of these approaches have failed to give priority to end-to-end communication reliability, or consider how it can support resilience.…”

“…In the context of VANETs, predicting traffic helps in the selection of channels. Thus, the reliability prediction procedure assists in managing the channel selection since it satisfies the different requirements and purposes of the nodes [22,25]. However, until now, prediction has not been employed to help in channel selection by seeking to improve reliability in data delivery.…”

A cross-layer adaptive channel selection mechanism for IEEE 802.11P suite

“…On the other hand, studies that focus on predictions taking advantage of Markovian assumption rely heavily on selecting the appropriate length of both prediction period and history. In the absence of historical data (past observations), it is reported that prediction accuracy degrades dramatically [22]. Studies which follow decision-making processes based on a posteriori probabilities of the occupancy state of a channel could also be considered in this manner.…”

“…Also, note that the collision avoidance mechanism in IEEE 802.11p uses carrier sense multiple access/collision avoidance (CSMA/CA) and can only detect known type of signals [9]. In the literature, there are mainly four categories in selecting a service channel for VANET systems: pre-allocation-based [15][16][17][18][19], randomized rotation-based [20], minimum duration-based [21], and predictive-based schemes [22]. Pre-allocation-based schemes employ a static database and then select a channel based on that database.…”